Browse
Submit Data
Databases
API
Help

Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptional profiling of HT-29 cells stimulated with pairwise combinations of TNF, EGF, and insulin

ABSTRACT: The objective of this study was to investigate the link between signaling-network activation and transcriptional regulation downstream of tumor necrosis factor (TNF), epidermal growth factor (EGF), and insulin. Microarrays were used to capture the transcriptional responses to TNF, EGF, and insulin at intermediate-to-late times after stimulation. Nine combinations of TNF, EGF, and insulin were evaluated at three time points after stimulation in biological duplicate.

ORGANISM(S): Homo sapiens

SUBMITTER: Kevin Janes

PROVIDER: E-GEOD-69639 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Publications

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors.

Chitforoushzadeh Zeinab Z Ye Zi Z Sheng Ziran Z LaRue Silvia S Fry Rebecca C RC Lauffenburger Douglas A DA Janes Kevin A KA

Science signaling 20160607 431

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integ ...[more]

PMID: 27273097

Similar Datasets

Transcriptional profiling of HT-29 cells stimulated with pairwise combinations of TNF, EGF, and insulin

Project description:The objective of this study was to investigate the link between signaling-network activation and transcriptional regulation downstream of tumor necrosis factor (TNF), epidermal growth factor (EGF), and insulin. Microarrays were used to capture the transcriptional responses to TNF, EGF, and insulin at intermediate-to-late times after stimulation.

2016-07-25 | GSE69639 | GEO

Transcriptional profiling of inducible GATA6L-overexpressing HT-29 cells stimulated with TNF

Project description:The objective of this study was to investigate the role of GATA6L with or without TNF stimulation. Microarrays were used to capture the transcriptional response to GATA6L overpexression at intermediate times after TNF stimulation. Cells were induced for GATA6L overexpression and evaluated at two hours after stimulation with saturating TNF in biological triplicate.

2016-07-25 | E-GEOD-72079 | biostudies-arrayexpress

Transcription profiling of human MCF10A mammary gland epithelial cells stimulated with epidermal growth factor vs unstimulated and sampled at multiple time points to investidage the temporal regulation of the cellular EGFR cascade

Project description:MCF10A cells derived from spontaneously immortalized normal human mammary epithelia were subjected to EGF/SERUM stimulation for 0,20,40,60,120,240 and 480 minutes. We used microarrays to understand the temporal regulation of the cellular EGFR cascade. Experiment Overall Design: MCF10A cells were grown in DME:F12 medium supplemented with antibiotics, [10 mg/ml insulin, 0.1 mg/ml cholera toxin, 0.5 mg/ml hydrocortisone, heat-inactivated horse serum (5%); defined as 'serum' in the text] and 10ng/ml EGF. Cells were serum deprived for 24 hours. Following stimulation with EGF/SERUM for 0,20,40,60,120,240 and 480 minutes.

2007-11-01 | E-GEOD-6784 | biostudies-arrayexpress

Homo sapiens

Project description:MCF7 EGF, HRG stimulation

| PRJNA112545 | ENA

Homo sapiens

Project description:H1299 EGF and Iressa stimulation

| PRJNA106031 | ENA

Homo sapiens

Project description:Transcriptional profiling of HT-29 cells stimulated with pairwise combinations of TNF, EGF, and insulin

| PRJNA286072 | ENA

iRhom2 Promotes Lupus Nephritis through ADAM17-Dependent TNF-α and EGFR Signaling

Project description:Lupus nephritis (LN) often results in progressive renal dysfunction. The inactive Rhomboid 2 (iRhom2) is a newly identified key regulator of A disintegrin and metalloprotease 17 (ADAM17), whose substrates, such as TNF-α and heparin-binding EGF (HB-EGF), have been implicated in the pathogenesis of chronic kidney disease. Here we demonstrate that deficiency of iRhom2 protects the lupus-prone Fcgr2b-/- mice from developing severe kidney damage without altering anti-double stranded (ds) DNA Ab production, by simultaneously blocking the HB-EGF/EGFR and the TNF-α signaling in the kidney tissues. Unbiased transcriptome profiling of kidneys and kidney macrophages revealed that TNF-α and HB-EGF/EGFR signaling pathways are highly upregulated in Fcgr2b-/- mice; alterations that were diminished in the absence of iRhom2. Pharmacological blockade of either TNF-α or EGFR signaling protected Fcgr2b-/- mice from severe renal damage. Finally, kidneys from LN patients showed increased iRhom2 and HB-EGF expression, with interstitial HB-EGF expression significantly associated with chronicity indices. Our data suggest that activation of iRhom2/ADAM17-dependent TNF-α and EGFR signaling plays a crucial role in mediating irreversible kidney damage in LN, thereby uncovering a novel target for selective and simultaneous dual inhibition of two major pathological pathways in the effector arm of the disease.

2019-01-01 | GSE107705 | GEO

HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations.

Project description:Detecting single nucleotide polymorphisms' (SNPs) interaction is one of the most popular approaches for explaining the missing heritability of common complex diseases in genome-wide association studies. Many methods have been proposed for SNP interaction detection, but most of them only focus on pairwise interactions and ignore high-order ones, which may also contribute to complex traits. Existing methods for high-order interaction detection can hardly handle genome-wide data and suffer from low detection power, due to the exponential growth of search space. In this paper, we proposed a flexible two-stage approach (called HiSeeker) to detect high-order interactions. In the screening stage, HiSeeker employs the chi-squared test and logistic regression model to efficiently obtain candidate pairwise combinations, which have intermediate or significant associations with the phenotype for interaction detection. In the search stage, two different strategies (exhaustive search and ant colony optimization-based search) are utilized to detect high-order interactions from candidate combinations. The experimental results on simulated datasets demonstrate that HiSeeker can more efficiently and effectively detect high-order interactions than related representative algorithms. On two real case-control datasets, HiSeeker also detects several significant high-order interactions, whose individual SNPs and pairwise interactions have no strong main effects or pairwise interaction effects, and these high-order interactions can hardly be identified by related algorithms.

| S-EPMC5485517 | biostudies-literature

Prediction of ultra-high-order antibiotic combinations based on pairwise interactions.

Project description:Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.

| S-EPMC6370231 | biostudies-literature

Homo sapiens

Project description:Dynamic response of EGF stimulation in lung cancer cells [EGF]

| PRJNA328017 | ENA